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4 Method, page #1 1.Write a document explaining what to do and why Not in the document = not on the table. 2.Simulate beams of 10k events, wide distributions. 3.Use those to find useful variables for PID. Find combinations of detectors, such that given A, expect B. When finished, there should be no covariance between variables. 4.Make fits for all expected values, and create “discrepancy variables” 1-expected/measured. Zero means very muon like. 5.Run 120k events of muons per experimental scenario. ~ 2Gb of data per file 6.For every such scenario, also run 120k muons with 40 ns lifetime to generate background. Muons not decayed at TOF2 are filtered out of analysis.

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5 Method, page #2 7.Digitize every simulated beam. 8.Convert to ROOT trees, and tag good/bad event. 9.For every scenario, merge the muon sample with the background sample. 10.Train a Neural Net on the half of the merged & filtered sample (training sample). 11.Using the weights acquired by Neural Net, assign a weight all other events (the test sample). 12.Make cut on the weight such that signal efficiency is 99.90% 13.The same cut gives background rejection, and thus purity after PID.

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20 Stage 6 – TURTLE beam A TURTLE beam with the design presented at RAL Oct’05 is the most realistic beam available. –Requires a 7.6 mm diffuser after TOF1. P z,TOF1 = 236 ± 26 MeV/c, non-Gaussian. In order to make G4MICE place the diffuser, I had to place it at z=-6078 mm. –It should be at z=-6014.8 mm. –Chris Rogers says: Z=-6010mm -> -3.10 T Z=-6080mm -> -2.51 T “…should be okay for PID stuff, but may I still need to fix it for beam optics stuff.”

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24 Requirements for CKOV2 In order for an extra detector to be useful, it should have a background rejection capability (in order to meet safety requirement) as a function of input purity and previous background rejection. –While loosing no signal events. –Assuming BG rejection of CKOV2 not correlated to previous BG rejection:

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